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Hailing from the University of Michigan, Erin Kashawlic is a senior earth systems science engineering major with a concentration in climate physics.

Erin joined Professor Vonder Haar and research scientist John Forsythe researching different data assimilation schemes. Operational forecast centers are starting to rely more upon data assimilation in order to produce more accurate forecasts that are based on current observations. As of present, the transform scheme is used most widely. It minimizes the cost function with respect to ln(x), as opposed to x, and then changes it back to the x space to complete the new forward run. It also takes the observations and considers them to be in the ln(x) space.

Fletcher and Zupanski (2006,2007) have developed the mixed DA system in which the minimzation as well as the observations are kept in the x space. With this, there is no need to convert into a different space, thus retaining more information, which is used to produce, in theory, a more accurate forecast.

Erin looked at the short-, medium- and long-term forecasts with each scheme and compared the differences to the true solution both for small and large observational errors. In the short- and medium-term forecasts, either scheme shows similar results, though mixed showed smaller peak amplitudes. In the long-term, however, either scheme is very chaotic. As more cycles are produced though, the mixed scheme becomes more accurate. Her research poster, A comparison between mixed and transform data assimilation schemes on short-, medium-, and long-term forecasts (5MB PDF).

Erin's other research interests include boundary layer cloud modeling, severe thunderstorm dynamics, tornadogenesis and public awareness/education of severe weather. Her hobbies include reading, watching movies, playing with her kitten (named after the Fujita scale, of course), video games and being involved in her sorority.